Modern hospitals utilize medical images from a variety of imaging devices such as, for example, a Computer Tomography (CT) scanner or a Magnetic Resonance Imaging (MRI) scanner producing volume datasets. Segmentation of volume datasets is an important task in medical diagnostics. Manual segmentation of large volume datasets is a slow process requiring expert knowledge in pathology and anatomy.
The results of computer-assisted techniques—for example, seeded region growing—are often highly variable when the Region Of Interest (ROI) is heterogeneous, making it difficult to segment lesions—for example, brain tumors—which have variable intensity, texture, shape, and size.
Computer-assisted level set segmentation techniques reduce the variability of difficult segmentation tasks. The level set segmentation is highly flexible since it depends on intrinsic factors—such as curvature of the segmented regions—and extrinsic factors—such as intensity or texture of the image. However, the flexibility of the level set segmentation results in high computational costs—long computation time and large memory requirements.
Two distinct processes for more efficiently determining the level set field update have been disclosed recently. The first is the narrow band process—disclosed in Adalsteinson D, Sethian J A “A Fast Level Set Method for Propagating Interfaces”, Journal of Computational Physics 8(2), pp. 269-277, 1995—where field updates are only determined on a narrow band of elements around an implicitly defined level set surface. Several processes for segmenting volume datasets have been implemented using the narrow band process. These processes use Graphics Processing Unit (GPU) virtual memory paging techniques for mapping the irregular and dynamic narrow band onto a physically contiguous domain suited for parallel computation on the GPU. A disadvantage of the narrow band process is that the entire computational domain—volume dataset—is frequently traversed in order to update the narrow band as the surface is deformed. The performance of GPU based narrow band processes is limited due to per-frame communication latency between the GPU and the Central Processing Unit (CPU), since the CPU is used for updating the narrow band, which has been only recently overcome by traversing the domain of active tiles in parallel on the GPU as disclosed in Jeong W-K, Beyer J, Hadwiger M, Vazquez A, Pfister H, and Whitaker R T “Scalable and Interactive Segmentation and Visualization of Neural Processes in EM datasets”, IEEE Transactions on Visualization and Computer Graphics 15, 6, pp. 1505-1514, 2009.
The second process is the sparse field process—disclosed in Whitaker, R “A Level Set Approach to 3D Reconstruction from Range Data”, International Journal of Computer Vision 29(3), pp. 203-231, 1998, and in Peng, D, Merriman, B, Osher, S, Zhao, H, and Kang, M “A PDE Based Fast Local Level Set Method”, Journal of Computational Physics 155(2), pp. 410-438, 1999. In contrast to the narrow band process the sparse field process incrementally updates a linked list of active elements at each time step. The sparse field process avoids traversing the entire computational domain except during initialization. Unfortunately, due to the reliance on linked lists state-of-the-art sparse field processes are ill-suited for parallel processing and are performed in a sequential fashion.
It is desirable to provide a parallelized sparse field level set method for segmenting a volume dataset.